In the last decade graphical models have become one of the most popular tools to structure uncertain knowledge about high-dimensional domains in order to make reasoning in such domains feasible. Their most prominent representatives are Bayesian networks and Markov networks, but also relational and possibilistic networks turned out to be useful in practical applications. For all types of networks several clear, correct, and efficient propagation methods have been developed, with join tree propagation and bucket elimination being among the most widely known.